Calculate the standard deviation of the firm's daily stock return over the past three months

[OBADA ALMAJALI]

Hello everyone,

I have daily panel data and I want to calculate the standard deviation of the firm's daily stock return over the past three months. Could anyone help me to find an appropriate code to calculate annualized 3-month rolling sample standard deviation and assuming the standard deviation is centered on zero, instead of centered around mean value given a time period.

Sample of my dataset is attached :



Kind Regards
Obada

[Zara Khan]

Dear Clyde/Nick/Attaullah

In reference to comment # 18 I am sharing my data.

* Example generated by -dataex-. To install: ssc install dataex
clear
input double permno long date double(vol shrout) float year
10001 16072 9470 2596 2004
10001 16075 7516 2596 2004
10001 16076 5743 2596 2004
10001 16077 6825 2596 2004
10001 16078 33624 2596 2004
10001 16079 11707 2596 2004
10001 16082 11026 2596 2004
10001 16083 15360 2596 2004
10001 16084 22025 2596 2004
10001 16085 5946 2596 2004
10001 16086 5425 2596 2004
10001 16090 6360 2596 2004
10001 16091 7845 2596 2004
10001 16092 12150 2596 2004
10001 16093 12200 2596 2004
10001 16096 11743 2596 2004
10001 16097 7200 2596 2004
10001 16098 4192 2596 2004
10001 16099 3625 2596 2004
10001 16100 5042 2596 2004
10001 16103 11650 2596 2004
10001 16104 9585 2596 2004
10001 16105 7250 2596 2004
10001 16106 3800 2596 2004
10001 16107 3300 2596 2004
10001 16110 2780 2596 2004
10001 16111 4800 2596 2004
10001 16112 950 2596 2004
10001 16113 5100 2596 2004
10001 16114 3400 2596 2004
10001 16118 4670 2596 2004
10001 16119 24030 2596 2004
10001 16120 7100 2596 2004
10001 16121 6055 2596 2004
10001 16124 3000 2596 2004
10001 16125 4725 2596 2004
10001 16126 800 2596 2004
10001 16127 2342 2596 2004
10001 16128 2328 2596 2004
10001 16131 7695 2596 2004
10001 16132 1100 2596 2004
10001 16133 9000 2596 2004
10001 16134 7100 2596 2004
10001 16135 4186 2596 2004
10001 16138 2713 2596 2004
10001 16139 6950 2596 2004
10001 16140 3584 2596 2004
10001 16141 900 2596 2004
10001 16142 11640 2596 2004
10001 16145 1800 2596 2004
10001 16146 19850 2596 2004
10001 16147 13600 2596 2004
10001 16148 1500 2596 2004
10001 16149 900 2596 2004
10001 16152 6400 2596 2004
10001 16153 7293 2596 2004
10001 16154 1550 2596 2004
10001 16155 4620 2596 2004
10001 16156 5550 2596 2004
10001 16159 10000 2596 2004
10001 16160 6150 2596 2004
10001 16161 915 2598 2004
10001 16162 330 2598 2004
10001 16163 3309 2598 2004
10001 16166 400 2598 2004
10001 16167 2009 2598 2004
10001 16168 1220 2598 2004
10001 16169 1600 2598 2004
10001 16173 400 2598 2004
10001 16174 1099 2598 2004
10001 16175 4200 2598 2004
10001 16176 2000 2598 2004
10001 16177 0 2598 2004
10001 16180 9828 2598 2004
10001 16181 5200 2598 2004
10001 16182 10911 2598 2004
10001 16183 7855 2598 2004
10001 16184 3800 2598 2004
10001 16187 4977 2598 2004
10001 16188 3659 2598 2004
10001 16189 3700 2598 2004
10001 16190 2418 2598 2004
10001 16191 600 2598 2004
10001 16194 300 2598 2004
10001 16195 6800 2598 2004
10001 16196 5000 2598 2004
10001 16197 900 2598 2004
10001 16198 3850 2598 2004
10001 16201 2300 2598 2004
10001 16202 1900 2598 2004
10001 16203 538 2598 2004
10001 16204 2730 2598 2004
10001 16205 1240 2598 2004
10001 16208 1800 2598 2004
10001 16209 9945 2598 2004
10001 16210 1650 2598 2004
10001 16211 2106 2598 2004
10001 16212 1500 2598 2004
10001 16215 100 2598 2004
10001 16216 8782 2598 2004
end
format %d date
[/CODE]

I have the data in a daily format and through commands mentioned in comment #18, I am converting it monthly frequency. with those commands, the data is converted into monthly form but the issue is my no of observations are reduced to a very small number. I have to use this monthly share turnover variable with other variables that are on an annual basis. The observations of other variables are around 18000 while this variable only has 132 observations which are not achieving my goals.

I need some suggestions from you people that what should I do in this regard. In addition, I have to calculate the average value of the monthly share turnover in order to merge it with the other annual variables of my estimations. Kindly assist me in this regard.

[Attaullah Shah]

The following two blog posts discuss the conversion of daily data to a weekly or monthly frequency, both for stock prices and stock returns

https://fintechprofessor.com/ascol-s...y-and-monthly/

https://fintechprofessor.com/2017/10...ct-of-returns/

[Clyde Schechter]

Example data using -dataex-, please.

[Zara Khan]

Dear Nick Cox and Clyde Schechter

I have daily data of stock returns and I want to convert its frequency to weekly returns. can you please help me out in this regard by telling the appropriate code for it?

[Nick Cox]

I can't comment on #18's correctness except that a monthly date variable would be better formatted as %tm not %td.

[Zara Khan]

Thank you so much, Clyde, for your assistance. It really worked for me. I need some further help in the generation of another variable. It is as follows:

DturnT: The difference between the average monthly share turnover over fiscal year T−1 and the average monthly share turnover over fiscal year T, where monthly share turnover is calculated as the monthly trading volume divided by the total number of shares outstanding over the month.

I have used the following commands for generating this variable.

gen month=mofd(date)
format month %td
bys month (date): keep if _n==_N
tsset month
**gen monthly share turnover
gen mst= vol/ shrout
**change in monthly share turnover
gen d_mst=d.mst

Kindly let me know whether the commands that I am using are correct enough for creating my desired variable.

[Clyde Schechter]

If I understand you correctly:

by permno year, sort: egen crash_variabe = max(return < mean - 3.09*sd)

Replace "return" by the actual name of the variable containing the weekly return.

[Zara Khan]

Dear Nick Cox and Clyde Schechter

I am calculating the stock price crash risk. The crash variable is measured as: An indicator variable that equals one if a firm experiences one or more firm-specific weekly returns exceeding 3.09
standard deviations below the mean firm-specific weekly returns over the fiscal year and zero otherwise, with 3.09 chosen to generate frequencies of 0.1% in a normal distribution. I am having the firm-specific weekly returns and i have calculated the standard deviation and mean by the following commands:

egen sd=sd(rret), by(permno year)

egen mean=mean(rret), by(permno year)

I want to ask that how I will generate the indicator variable to 1 if a firm experiences one or more firm-specific weekly returns exceeding 3.09 standard deviations below the mean firm-specific weekly returns. Kindly assist me in this regard.

Regards

[Celine Tran]

Dear Clyde Schechter ,

I appreciate your help. The main question I would like to ask is related to calcuate standard deviation at #10.

I though that I can deal with "reshape" by myself ..., so that I post the question about standard deviation here. I am sorry.

Regarding your code to reshape the panel data, it seems not to work with my data. I post in a new topic and would like to mention you there.

I will back to this topic about standard deviation later.

One a gain, thank you so much.

[Clyde Schechter]

This is a -reshape- problem, not a transposition.

rename A str_date
foreach v of varlist IM-IP {
    rename `v' price_`=strtoname(`v'[1])'
}
drop in 1
destring price_*, replace
gen date = daily(str_date, "MDY")
assert missing(date) == missing(str_date)
format date %td
drop str_date

reshape long price_, i(date) j(firm) string
rename price_ price

This question really does not belong in this thread; it is off topic. While it is easy to think of these threads as dialogs between a questioner and a responder, in fact there are many others who read long on selected topics of interest, or who come and search for answers to their own questions in earlier posts. For the sake of those people and not wasting their time, it is important to keep threads on topic. In the future, when you are posing a question that is not clearly related to an existing thread, start a New Topic instead.

[Celine Tran]

Dear Nick Cox and Clyde Schechter ,

For #12, I collect weekly stock price form Datastream. All data, including datem, is automatically download from Datastream. The raw data looks like:

input str10 A str17(IM IP)
"Code"       "DISA.SI(RI)~U$"    "SUNR.SI(RI)~U$"  
"  1/1/1998" "20.29"             "47.43"          
"  1/8/1998" "17.06"             "28.04"          
" 1/15/1998" "15.81"             "42.54"          
" 1/22/1998" "15.82"             "42.56"          
" 1/29/1998" "16.15"             "43.44"          
"  2/5/1998" "24.41"             "45.14"          
" 2/12/1998" "23.77"             "63.54"          
" 2/19/1998" "22.6"              "64.55"          
" 2/26/1998" "26.52"             "64.63"          
"  3/5/1998" "21.5"              "63.61"          
" 3/12/1998" "21.09"             "64.70999999999999"
" 3/19/1998" "22.07"             "65.3"            
" 3/26/1998" "23.01"             "62.59"          
"  4/2/1998" "18.72"             "61.53"          
"  4/9/1998" "18.3"              "39.24"          
" 4/16/1998" "16.59"             "65.44"          
" 4/23/1998" "16.73"             "50.3"            
" 4/30/1998" "16.79"             "50.47"          
"  5/7/1998" "14.8"              "38.41"          
" 5/14/1998" "13.76"             "48.24"          
" 5/21/1998" "15.49"             "48.88"          
" 5/28/1998" "12.1"              "41.76"          
"  6/4/1998" "11.26"             "42.94"          
" 6/11/1998" "10.18"             "35.84"          
" 6/18/1998" "11.38"             "37.41"          
" 6/25/1998" "10.65"             "36.75"          
"  7/2/1998" "9.66"              "47.02"          
"  7/9/1998" "10.32"             "43.63"          
" 7/16/1998" "9.060000000000001" "40.04"          
" 7/23/1998" "8.140000000000001" "42.27"          
" 7/30/1998" "7.37"              "42.1"            
"  8/6/1998" "8"                 "41.55"          
" 8/13/1998" "7.25"              "37"              
" 8/20/1998" "7.98"              "36.99"          
" 8/27/1998" "7.88"              "36.52"          
"  9/3/1998" "7.94"              "35.34"          
" 9/10/1998" "8.050000000000001" "35.83"          
" 9/17/1998" "8.82"              "36"              
" 9/24/1998" "7.37"              "39.1"            
" 10/1/1998" "7.5"               "39.78"          
" 10/8/1998" "6.91"              "35.27"          
"10/15/1998" "8.66"              "40.14"          
"10/22/1998" "9.380000000000001" "40.67"          
"10/29/1998" "11.73"             "37.49"          
" 11/5/1998" "12.5"              "47.83"          
"11/12/1998" "10.69"             "51.39"          
"11/19/1998" "11.71"             "54.14"          
"11/26/1998" "11.5"              "53.21"          
" 12/3/1998" "10.78"             "56.54"          
"12/10/1998" "11.66"             "57.1"            
"12/17/1998" "11.58"             "44.11"          
"12/24/1998" "10.77"             "43.94"          
"12/31/1998" "10.74"             "43.84"          
"  1/7/1999" "11.37"             "43.29"          
" 1/14/1999" "13.56"             "54.56"          
" 1/21/1999" "13.64"             "47.16"          
" 1/28/1999" "11.98"             "45.85"          
"  2/4/1999" "12"                "39.04"          
" 2/11/1999" "12"                "42.86"          
" 2/18/1999" "13.4"              "42.54"          
" 2/25/1999" "12.47"             "37.43"          
"  3/4/1999" "13.13"             "37.21"          
" 3/11/1999" "13.13"             "37.2"            
" 3/18/1999" "12.47"             "37.42"          
" 3/25/1999" "13.93"             "41.89"          
"  4/1/1999" "13.19"             "37.37"          
"  4/8/1999" "14.64"             "41.82"          
" 4/15/1999" "14.96"             "55.7"            
" 4/22/1999" "22.97"             "55.95"          
" 4/29/1999" "20.15"             "51.78"          
"  5/6/1999" "23.08"             "53.94"          
" 5/13/1999" "22.27"             "46.95"          
" 5/20/1999" "22.81"             "52.55"          
" 5/27/1999" "26.33"             "74.63"          
"  6/3/1999" "30.08"             "79.34"          
" 6/10/1999" "35.43"             "78.33"          
" 6/17/1999" "55.08"             "83.54000000000001"
" 6/24/1999" "62.5"              "91.84999999999999"
"  7/1/1999" "72.31999999999999" "108.02"          
"  7/8/1999" "55.18"             "81.41"          
" 7/15/1999" "44.74"             "57.82"          
" 7/22/1999" "41.01"             "56.3"            
" 7/29/1999" "42.76"             "59.69"          
"  8/5/1999" "39.35"             "55.59"          
" 8/12/1999" "34.06"             "52.51"          
" 8/19/1999" "36.99"             "55.45"          
" 8/26/1999" "47.15"             "65.67"          
"  9/2/1999" "42.84"             "56.74"          
"  9/9/1999" "42.77"             "58.18"          
" 9/16/1999" "41.89"             "58"              
" 9/23/1999" "41.37"             "54.27"          
" 9/30/1999" "37.23"             "53.18"          
" 10/7/1999" "40.71"             "55.38"          
"10/14/1999" "39.97"             "53.86"          
"10/21/1999" "37.92"             "51.07"          
"10/28/1999" "36.45"             "50.36"          
" 11/4/1999" "40.27"             "55.82"          
"11/11/1999" "46.1"              "60.92"          
"11/18/1999" "45.49"             "59.56"          
end

Because the raw date is exported from Datastream in a form of row, the first column is Date, the sencond column is stock price of firm 1, the thirst column is for firm 2 and so on.....

Before calculating the standard deviation that I mentioned in #10, I need to format the raw data. In details, I need to transpose the panel data to the form that all firms are in one column, and then the next column is Date. But I do not know how to do. Could you please help? The one I post in #10 is from my mamuanlly transposing.

Thank you very much in advance.

Regards,
Celine

[Clyde Schechter]

However, there are some problem here. The number of weeks for each year maybe different . I means it is not always 52 weeks each year. So, I do not know what should I do.

Well, imagine for the moment that you were going to do these calculations by hand. (To be very, very clear, I am not recommending you actually do that--this is a thought experiment.) How would you know which weeks to include in each case? If you can explain that, it will probably not be too hard to write code that corresponds to it. Looking at your example data, it is not obvious to me why the number of weeks should vary from one instance to another, nor where the information defining the included weeks would come from.

[Nick Cox]

https://www.statalist.org/forums/help#stata is the link I alluded to earlier, which tells you how to improve your data examples.

[Celine Tran]

Dear Nick Cox ,

Thank you for your reply. I do not ask about specialist calculations. I understand what your responese at #7. Therefore, ,my question is different. I would like to know how to use rangestat to calulate the standard deviation of certain number of obs.

Here is my data.

TRCode Date return
GPRL 1/2/2014 
GPRL 1/9/2014 .0308133
GPRL 1/16/2014 -.0013903
GPRL 1/23/2014 -.03063
GPRL 1/30/2014 .0233393
GPRL 2/6/2014 -.0614035
GPRL 2/13/2014 .0007477
GPRL 2/20/2014 .0085917
GPRL 2/27/2014 -.0003704
GPRL 3/6/2014 .1081882
GPRL 3/13/2014 .0177198
GPRL 3/20/2014 -.0466491
GPRL 3/27/2014 .0434183
GPRL 4/3/2014 .0904888
GPRL 4/10/2014 .0529982
GPRL 4/17/2014 -.0353753
GPRL 4/24/2014 .0399523
GPRL 5/1/2014 .016055
GPRL 5/8/2014 -.0513544
GPRL 5/15/2014 .0960738
GPRL 5/22/2014 .1082768
GPRL 5/29/2014 -.0022037
GPRL 6/5/2014 -.0262577
GPRL 6/12/2014 -.0438508
GPRL 6/19/2014 -.0005271
GPRL 6/26/2014 .0780591
GPRL 7/3/2014 .0273973
GPRL 7/10/2014 .0754762
GPRL 7/17/2014 .0225814
GPRL 7/24/2014 .0441654
GPRL 7/31/2014 -.0470661
GPRL 8/7/2014 -.0461271
GPRL 8/14/2014 -.0866788
GPRL 8/21/2014 -.017982
GPRL 8/28/2014 .0155137
GPRL 9/4/2014 -.0042575
GPRL 9/11/2014 -.02666
GPRL 9/18/2014 .0173127
GPRL 9/25/2014 -.001016
GPRL 10/2/2014 -.0030511
GPRL 10/9/2014 .0015302
GPRL 10/16/2014 -.1033868
GPRL 10/23/2014 -.0227208
GPRL 10/30/2014 .0784656
GPRL 11/6/2014 -.0285637
GPRL 11/13/2014 .0183079
GPRL 11/20/2014 -.0332335
GPRL 11/27/2014 -.0369118
GPRL 12/4/2014 -.0263312
GPRL 12/11/2014 -.0090144
GPRL 12/18/2014 -.0451789
GPRL 12/25/2014 -.0123849
GPRL 1/1/2015 -.0318328
GPRL 1/8/2015 -.0099635
GPRL 1/15/2015 .0073801
GPRL 1/22/2015 .038628
GPRL 1/29/2015 .0057711
GPRL 2/5/2015 .0044629
GPRL 2/12/2015 -.0729927
GPRL 2/19/2015 .0619651
GPRL 2/26/2015 .0367505
GPRL 3/5/2015 -.0951493
GPRL 3/12/2015 .0683849
GPRL 3/19/2015 .0331296
GPRL 3/26/2015 .0554172
GPRL 4/2/2015 .0073746
GPRL 4/9/2015 .0430454
GPRL 4/16/2015 .0235823
GPRL 4/23/2015 .0112452
GPRL 4/30/2015 .0008137
GPRL 5/7/2015 -.0468835
GPRL 5/14/2015 .005118
GPRL 5/21/2015 -.0523338
GPRL 5/28/2015 .0295522
GPRL 6/4/2015 .0034793
GPRL 6/11/2015 .0167582
GPRL 6/18/2015 .0048309
GPRL 6/25/2015 -.0079186
GPRL 7/2/2015 -.0142531
GPRL 7/9/2015 -.0575477
GPRL 7/16/2015 -.0104326
GPRL 7/23/2015 -.020155
GPRL 7/30/2015 -.0170886
GPRL 8/6/2015 .0534449
GPRL 8/13/2015 -.021088
GPRL 8/20/2015 -.049641
GPRL 8/27/2015 -.0588042
GPRL 9/3/2015 -.0104712
GPRL 9/10/2015 -.038448
GPRL 9/17/2015 .0172414
GPRL 9/24/2015 .0151461
GPRL 10/1/2015 -.0017762
GPRL 10/8/2015 .03879
GPRL 10/15/2015 .0226105
GPRL 10/22/2015 -.0107203
GPRL 10/29/2015 -.1381646
GPRL 11/5/2015 -.0227898
GPRL 11/12/2015 .0261359
GPRL 11/19/2015 -.0525078
GPRL 11/26/2015 -.0107527
GPRL 12/3/2015 -.0426421
GPRL 12/10/2015 .0004367
GPRL 12/17/2015 -.045395
GPRL 12/24/2015 -.0310928
GPRL 12/31/2015 .0122699
GPRL 1/7/2016 -.0097902
GPRL 1/14/2016 .0390772
GPRL 1/21/2016 .0616221
GPRL 1/28/2016 .0072557
GPRL 2/4/2016 .0224576
GPRL 2/11/2016 .002901
GPRL 2/18/2016 -.0082645
GPRL 2/25/2016 .0004167
GPRL 3/3/2016 .18409
GPRL 3/10/2016 -.2244108
GPRL 3/17/2016 .0435374
GPRL 3/24/2016 -.0147762
GPRL 3/31/2016 .0816056
GPRL 4/7/2016 -.0619902
GPRL 4/14/2016 .0530435
GPRL 4/21/2016 -.0082576
GPRL 4/28/2016 .0399667
GPRL 5/5/2016 .0064051
GPRL 5/12/2016 -.0453461
GPRL 5/19/2016 -.0079167
GPRL 5/26/2016 .0041999
GPRL 6/2/2016 .019657
GPRL 6/9/2016 .4364233
GPRL 6/16/2016 -.1956025
GPRL 6/23/2016 .0099397
GPRL 6/30/2016 -.0042179
GPRL 7/7/2016 .204377
GPRL 7/14/2016 -.0521688
GPRL 7/21/2016 -.3107607
GPRL 7/28/2016 .0897263
GPRL 8/4/2016 -.0526966
GPRL 8/11/2016 -.0425902
GPRL 8/18/2016 .1130277
GPRL 8/25/2016 .0297716
GPRL 9/1/2016 .0154455
GPRL 9/8/2016 -.0292512
GPRL 9/15/2016 .0269184
GPRL 9/22/2016 .0449922
GPRL 9/29/2016 .0475477
GPRL 10/6/2016 .0268049
GPRL 10/13/2016 -.0090498
GPRL 10/20/2016 .0779768
GPRL 10/27/2016 .0130336
GPRL 11/3/2016 -.0681891
GPRL 11/10/2016 -.0176044
GPRL 11/17/2016 -.0049192
GPRL 11/24/2016 -.0105932
GPRL 12/1/2016 .0021413
GPRL 12/8/2016 .0192308
GPRL 12/15/2016 -.0132774
GPRL 12/22/2016 .0290368
GPRL 12/29/2016 .0141087
GPRL 1/5/2017 .0302002
GPRL 1/12/2017 .0032938
GPRL 1/19/2017 -.0338148
GPRL 1/26/2017 .0502888
GPRL 2/2/2017 .0116467
GPRL 2/9/2017 -.0070355
GPRL 2/16/2017 -.0144928
GPRL 2/23/2017 .0395425
GPRL 3/2/2017 .0543854
GPRL 3/9/2017 -.0068575
GPRL 3/16/2017 -.0141099
GPRL 3/23/2017 .0301462
GPRL 3/30/2017 -.0381318
GPRL 4/6/2017 .0236632
GPRL 4/13/2017 .0030021
GPRL 4/20/2017 .0565699
GPRL 4/27/2017 .0127479
GPRL 5/4/2017 .0237762
GPRL 5/11/2017 -.0795082
GPRL 5/18/2017 .0418522
GPRL 5/25/2017 .0635328
GPRL 6/1/2017 -.0008036
GPRL 6/8/2017 .016622
GPRL 6/15/2017 .0263713
GPRL 6/22/2017 .0465057
GPRL 6/29/2017 .0078566
GPRL 7/6/2017 -.0267966
GPRL 7/13/2017 .0297872
GPRL 7/20/2017 .033544
GPRL 7/27/2017 -.020461
GPRL 8/3/2017 -.0489796
GPRL 8/10/2017 -.01641
GPRL 8/17/2017 -.0277207
GPRL 8/24/2017 .029831
GPRL 8/31/2017 .0158934
GPRL 9/7/2017 -.0406258
GPRL 9/14/2017 -.0068385
GPRL 9/21/2017 -.0007945
GPRL 9/28/2017 -.0055659
GPRL 10/5/2017 -.0029318
GPRL 10/12/2017 -.02085
GPRL 10/19/2017 -.015834
GPRL 10/26/2017 -.0061026
GPRL 11/2/2017 .0178621
GPRL 11/9/2017 -.0008226
GPRL 11/16/2017 .0321076
GPRL 11/23/2017 .0071789
GPRL 11/30/2017 -.0285111
GPRL 12/7/2017 .0548913
GPRL 12/14/2017 -.052035
GPRL 12/21/2017 -.0407609
GPRL 12/28/2017 .0048159
TISE 1/2/2014 
TISE 1/9/2014 -.0278686
TISE 1/16/2014 -.0013164
TISE 1/23/2014 .0458407
TISE 1/30/2014 -.0211455
TISE 2/6/2014 -.0187411
TISE 2/13/2014 .0508821
TISE 2/20/2014 -.0226138
TISE 2/27/2014 .0242725
TISE 3/6/2014 -.0467018
TISE 3/13/2014 -.0267481
TISE 3/20/2014 -.0089619
TISE 3/27/2014 -.0001507
TISE 4/3/2014 .0064818
TISE 4/10/2014 .0220159
TISE 4/17/2014 .0033705
TISE 4/24/2014 -.0163575
TISE 5/1/2014 .009948
TISE 5/8/2014 .0263158
TISE 5/15/2014 -.0508523
TISE 5/22/2014 -.0105644
TISE 5/29/2014 .0359976
TISE 6/5/2014 .0136926
TISE 6/12/2014 .042992
TISE 6/19/2014 -.0403843
TISE 6/26/2014 -.0007256
TISE 7/3/2014 .0027592
TISE 7/10/2014 .0194062
TISE 7/17/2014 -.0099446
TISE 7/24/2014 .0007175
TISE 7/31/2014 -.0055922
TISE 8/7/2014 -.0027397
TISE 8/14/2014 -.0005784
TISE 8/21/2014 .0021701
TISE 8/28/2014 -.0153024
TISE 9/4/2014 -.0042516
TISE 9/11/2014 -.0278269
TISE 9/18/2014 -.0137816
TISE 9/25/2014 -.0116708
TISE 10/2/2014 .0023306
TISE 10/9/2014 -.0148814
TISE 10/16/2014 -.0314713
TISE 10/23/2014 -.029082
TISE 10/30/2014 -.001004
TISE 11/6/2014 .0335008
TISE 11/13/2014 -.0427877
TISE 11/20/2014 .0220115
TISE 11/27/2014 .018224
TISE 12/4/2014 -.0261959
TISE 12/11/2014 -.0203843
TISE 12/18/2014 .0006822
TISE 12/25/2014 -.0352821
TISE 1/1/2015 .0641343
TISE 1/8/2015 -.0654159
TISE 1/15/2015 .0191864
TISE 1/22/2015 -.0282378
TISE 1/29/2015 -.0001794
TISE 2/5/2015 .0405454
TISE 2/12/2015 -.0432759
TISE 2/19/2015 -.0720851
TISE 2/26/2015 .0075743
TISE 3/5/2015 -.0354665
TISE 3/12/2015 -.044964
TISE 3/19/2015 -.000837
TISE 3/26/2015 .0433508
TISE 4/2/2015 .007226
TISE 4/9/2015 -.0984456
TISE 4/16/2015 .061229
TISE 4/23/2015 .0131223
TISE 4/30/2015 -.0117188
TISE 5/7/2015 -.0062409
TISE 5/14/2015 -.025539
TISE 5/21/2015 .0227712
TISE 5/28/2015 -.0247847
TISE 6/4/2015 .003446
TISE 6/11/2015 .0002146
TISE 6/18/2015 .0834764
TISE 6/25/2015 -.0883343
TISE 7/2/2015 .011297
TISE 7/9/2015 -.0238453
TISE 7/16/2015 -.0103433
TISE 7/23/2015 .019791
TISE 7/30/2015 -.0196249
TISE 8/6/2015 -.0507117
TISE 8/13/2015 .0513121
TISE 8/20/2015 -.0109204
TISE 8/27/2015 -.3050924
TISE 9/3/2015 .0311284
TISE 9/10/2015 .0003145
TISE 9/17/2015 .0204338
TISE 9/24/2015 .0070856
TISE 10/1/2015 .3386357
TISE 10/8/2015 -.226691
TISE 10/15/2015 .0227541
TISE 10/22/2015 .0366946
TISE 10/29/2015 .0604794
TISE 11/5/2015 -.0010512
TISE 11/12/2015 .2423047
TISE 11/19/2015 -.2166455
TISE 11/26/2015 .0481211
TISE 12/3/2015 .0247614
TISE 12/10/2015 -.0201359
TISE 12/17/2015 -.0133573
TISE 12/24/2015 .0106743
TISE 12/31/2015 .0528078
TISE 1/7/2016 -.0680206
TISE 1/14/2016 -.003413
TISE 1/21/2016 -.1454162
TISE 1/28/2016 .0317509
TISE 2/4/2016 .1443083
TISE 2/11/2016 .024282
TISE 2/18/2016 -.1738465
TISE 2/25/2016 .1255785
TISE 3/3/2016 .0317982
TISE 3/10/2016 .0286929
TISE 3/17/2016 .0433884
TISE 3/24/2016 -.0556931
TISE 3/31/2016 .0180865
TISE 4/7/2016 .0399073
TISE 4/14/2016 -.0505076
TISE 4/21/2016 .011734
TISE 4/28/2016 0
TISE 5/5/2016 -.075
TISE 5/12/2016 .0401226
TISE 5/19/2016 .0375033
TISE 5/26/2016 .091402
TISE 6/2/2016 -.0603265
TISE 6/9/2016 .0392749
TISE 6/16/2016 -.0016957
TISE 6/23/2016 .009949
TISE 6/30/2016 -.0456511
TISE 7/7/2016 .041289
TISE 7/14/2016 .0050774
TISE 7/21/2016 -.0108251
TISE 7/28/2016 .0026751
TISE 8/4/2016 -.0133398
TISE 8/11/2016 .0427729
TISE 8/18/2016 .0014144
TISE 8/25/2016 -.0301318
TISE 9/1/2016 -.0245146
TISE 9/8/2016 .0303558
TISE 9/15/2016 -.0326008
TISE 9/22/2016 .0072391
TISE 9/29/2016 .0146221
TISE 10/6/2016 -.0075721
TISE 10/13/2016 -.0504553
TISE 10/20/2016 -.0049248
TISE 10/27/2016 .0630372
TISE 11/3/2016 -.1359961
TISE 11/10/2016 .1463415
TISE 11/17/2016 -.065809
TISE 11/24/2016 -.0323093
TISE 12/1/2016 .0021894
TISE 12/8/2016 .0027307
TISE 12/15/2016 .0087146
TISE 12/22/2016 -.0029698
TISE 12/29/2016 -.0232873
TISE 1/5/2017 .03715
TISE 1/12/2017 .0248597
TISE 1/19/2017 -.0245175
TISE 1/26/2017 -.018984
TISE 2/2/2017 -.0106296
TISE 2/9/2017 .038292
TISE 2/16/2017 -.0212258
TISE 2/23/2017 .0759013
TISE 3/2/2017 .0788612
TISE 3/9/2017 -.0450724
TISE 3/16/2017 .0141844
TISE 3/23/2017 .0217024
TISE 3/30/2017 -.0160491
TISE 4/6/2017 .0741185
TISE 4/13/2017 .0031264
TISE 4/20/2017 -.0552093
TISE 4/27/2017 .0188501
TISE 5/4/2017 -.0407031
TISE 5/11/2017 .0460463
TISE 5/18/2017 .0313436
TISE 5/25/2017 .024581
TISE 6/1/2017 -.0008724
TISE 6/8/2017 .0406025
TISE 6/15/2017 -.0004196
TISE 6/22/2017 .0304302
TISE 6/29/2017 -.0099796
TISE 7/6/2017 .0514297
TISE 7/13/2017 .1062414
TISE 7/20/2017 .0083127
TISE 7/27/2017 -.0114015
TISE 8/3/2017 .0168559
TISE 8/10/2017 -.0342
TISE 8/17/2017 -.0325203
TISE 8/24/2017 -.0304388
TISE 8/31/2017 .0025039
TISE 9/7/2017 .081268
TISE 9/14/2017 -.0069296
TISE 9/21/2017 .0309537
TISE 9/28/2017 -.0208261
TISE 10/5/2017 .0436016
TISE 10/12/2017 .0823709
TISE 10/19/2017 .0400126
TISE 10/26/2017 -.006035
TISE 11/2/2017 .0034912
TISE 11/9/2017 .0654969
TISE 11/16/2017 -.0340715
TISE 11/23/2017 -.0191064
TISE 11/30/2017 .0394067
TISE 12/7/2017 .0106674
TISE 12/14/2017 -.0359435
TISE 12/21/2017 .0285545
TISE 12/28/2017 -.0208573

I only provide the weekly data for 2 companies from 2014 to 2017. Now, I would like to calculate the variable which is standard deviations of previous two years' stock return. For example, the value of this variable of company GPRL for year 2016 will be the standard devidation of previous 104 weeks (from year 2014 to year 2015, I count that there are 104 weeks, each year has 52 weeks ).

However, there are some problem here. The number of weeks for each year maybe different . I means it is not always 52 weeks each year. So, I do not know what should I do.

Could you please help. Thank you very much in advance.

I do apologise because my data is messy. Honestly, I do not how to post it here as others do.